PDF Version DemoDatabricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5 |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Associate |
| Exam Duration: | 90 minutes |
| Passing Score: | Not publicly disclosed |
| Available Languages: | English |
| Exam Format: | Multiple Choice, Multiple Select |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 45 |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored exam via Kryterion Webassessor or authorized test center. |
| Pre Condition: | No formal prerequisites. Databricks recommends at least 6 months of hands-on Apache Spark experience and Python programming knowledge. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 2: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 3: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 4: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 5: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 6: Structured Streaming | 10% | - Streaming Applications
|
| Topic 7: Using Spark SQL | 20% | - Spark SQL Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
1. 43 of 55.
An organization has been running a Spark application in production and is considering disabling the Spark History Server to reduce resource usage.
What will be the impact of disabling the Spark History Server in production?
A) Improved job execution speed due to reduced logging overhead
B) Prevention of driver log accumulation during long-running jobs
C) Enhanced executor performance due to reduced log size
D) Loss of access to past job logs and reduced debugging capability for completed jobs
2. 32 of 55.
A developer is creating a Spark application that performs multiple DataFrame transformations and actions. The developer wants to maintain optimal performance by properly managing the SparkSession.
How should the developer handle the SparkSession throughout the application?
A) Avoid using a SparkSession and rely on SparkContext only.
B) Use a single SparkSession instance for the entire application.
C) Create a new SparkSession instance before each transformation.
D) Stop and restart the SparkSession after each action.
3. 10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?
A) It is available only with Python, thereby reducing the learning curve.
B) It computes results immediately using eager execution.
C) It runs on a single node only, utilizing memory efficiently.
D) It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
4. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?
A) streaming_df.filter("count < 30")
B) streaming_df.count()
C) streaming_df.show()
D) streaming_df.select(countDistinct("name"))
5. A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
A) By configuring the option checkpointLocation during readStream
B) By configuring the option recoveryLocation during the SparkSession initialization
C) By configuring the option checkpointLocation during writeStream
D) By configuring the option recoveryLocation during writeStream
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: C |
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